IB919-15 Advanced Analytics: Models and Applications
Introductory description
This module is designed to introduce students to advanced analytics using different optimisation models, and to demonstrate them with applications ranging from healthcare, sports and social network, to asset management and fraud detection, presented using different case studies and examples,
Module aims
This module will offer students another perspective on analytics. The module covers several applications of analytics as well as the methodology behind these.
Outline syllabus
This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.
Proposed syllabus:
W1: Introduction: overview of analytics (models vs experts, analytical trend such as recommendation/personalised system)
W2: Linear and integer (linear) optimisation: a brief introduction
W3: Analytics for Kidney Allocation
W4: Online Advertising
W5: Analytics in Asset Management
W6: Combinatorial optimisation and heuristics: a brief introduction
W7: Optimising Sports League Structures
W8: Fraud Detection
W9: Network Science
Learning outcomes
By the end of the module, students should be able to:
- Understand the importance of optimisation models in different applications of analytics
- Understand simple optimisation models (linear, integer, and combinatorial optimisation)
- Critically analyse different case studies in analytics
- Written communication skills Numeracy Problem solving and modelling skills Teamwork skills
- Appreciate the power of analytics in different application domains ranging from healthcare, sports, social network, to asset management and fraud detection.
- Understand how optimisation models such as (integer) linear optimisation and combinatorial optimisation can be applied in analytics.
- Understand how analytics and optimisation can be applied in different application domain of analytics
Indicative reading list
- D. Bertsimas, A. O'Hair, and W. Pulleybank, The Analytics Edge, Dynamic Ideas, 2016 .
- D. Bertsimas and R. Freund, Data, Models, and Decisions, Athena, 2004 .
- D. Bertsimas and J. Tsitsiklis, Introduction to Linear Optimization, Athena, 1997 .
- C. Papadimitriou and K. Steiglitz, Combinatorial Optimization: Algorithms and Complexity, Dover Publications, 1998.
Subject specific skills
Apply optimisation techniques in different applications of analytics.
Transferable skills
Written communication skills .
Numeracy.
Problem solving and modeling skills.
Teamwork skills.
Study time
| Type | Required |
|---|---|
| Lectures | 9 sessions of 3 hours (18%) |
| Private study | 123 hours (82%) |
| Total | 150 hours |
Private study description
Self study to include pre-reading for lectures and preparation for assessment
Costs
No further costs have been identified for this module.
You do not need to pass all assessment components to pass the module.
Assessment group D1
| Weighting | Study time | Eligible for self-certification | |
|---|---|---|---|
Assessment component |
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| Group Project | 25% | No | |
Reassessment component is the same |
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Assessment component |
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| Written Examination - Local | 75% | No | |
Reassessment component is the same |
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Feedback on assessment
General question-by-question feedback for the whole cohort for the exam. For the group assessment, in addition to peer assessment, written feedback would be provided at the group level
Courses
This module is Optional for:
- Year 1 of TIBS-N1N3 Postgraduate Taught Business Analytics